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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction
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A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction

机译：通过促进分析和癌症药物敏感性预测的转移学习方法

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Transfer learning (TL) algorithms aim to improve the prediction performance in a target task (e.g. the prediction of cisplatin sensitivity in triple-negative breast cancer patients) via transferring knowledge from auxiliary data of a related task (e.g. the prediction of docetaxel sensitivity in breast cancer patients), where the distribution and even the feature space of the data pertaining to the tasks can be different. In real-world applications, we sometimes have a limited training set in a target task while we have auxiliary data from a related task. To obtain a better prediction performance in the target task, supervised learning requires a sufficiently large training set in the target task to perform well in predicting future test examples of the target task. In this paper, we propose a TL approach for cancer drug sensitivity prediction, where our approach combines three techniques. First, we shift the representation of a subset of examples from auxiliary data of a related task to a representation closer to a target training set of a target task. Second, we align the shifted representation of the selected examples of the auxiliary data to the target training set to obtain examples with representation aligned to the target training set. Third, we train machine learning algorithms using both the target training set and the aligned examples. We evaluate the performance of our approach against baseline approaches using the Area Under the receiver operating characteristic (ROC) Curve (AUC) on real clinical trial datasets pertaining to multiple myeloma, nonsmall cell lung cancer, triple-negative breast cancer, and breast cancer. Experimental results show that our approach is better than the baseline approaches in terms of performance and statistical significance.
机译：转移学习（TL）算法旨在通过从相关任务的辅助数据转移知识来改善目标任务中的预测性能（例如，三阴性乳腺癌患者中的顺铂敏感性的预测）（例如，在乳房中的多西紫杉醇敏感性预测癌症患者），其中分布甚至与任务有关的数据的特征空间可以不同。在真实的应用程序中，我们有时在目标任务中设置有限的培训，同时我们有来自相关任务的辅助数据。为了在目标任务中获得更好的预测性能，监督学习需要在目标任务中设置的足够大的训练，以便在预测目标任务的未来测试示例中进行良好。在本文中，我们提出了一种癌症药物敏感性预测的TL方法，我们的方法结合了三种技术。首先，我们将示例的子集的表示从相关任务的辅助数据转换到更靠近目标任务的目标训练集的表示。其次，我们将辅助数据的所选示例的移位表示对准到目标训练集，以获得与目标训练集合对齐的表示的示例。第三，我们使用目标训练集和对齐的示例训练机器学习算法。我们评估了我们使用与多种骨髓瘤，非骨骼细胞肺癌，三重阴性乳腺癌和乳腺癌有关的真正临床试验数据集的接收器操作特征（ROC）曲线（AUC）下的基线方法对基线方法的性能。实验结果表明，我们的方法优于表现和统计学意义方面的基线方法。

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